The purpose of this grant is to support the continued development and maintenance of DtiStudio/MriStudio/ MriCloud software family, which was developed at the PIs? lab in Johns Hopkins University. DtiStudio was introduced in 2001 and remains one of the most widely used programs to process diffusion tensor imaging (DTI) data. MriStudio, which consists of DiffeoMap and RoiEditor, was introduced in 2007 and provide a unique environment in which to perform a cutting-edge brain mapping and atlas-based image analysis. In 2014, we introduced an entirely new software platform, MriCloud, which is fully based on cloud architecture with a web- interface (www.mricloud.org). This platform not only integrated all the analysis offered by Dti/MriStudio in a fully automated manner but also provides new types of services that was made possible by the cloud architecture. Within two years, it has more than 1,200 registered users and the monthly processed data reached over 8,000 in April, 2017. There are currently four Software-as-a-Service (SaaS) provided in this platform. In this application, we propose to extend this service to the community through the following aims; Aim 1: Continued user support through update, education, and dissemination Dissemination: Currently, two major channels of dissemination are web-based resources (manuals and videos) and hands-on monthly three-day tutorials. Each tutorial accepts 12 applicants with 5 faculties and two programmers helping the attendees. As the functionalities of DtiStudio/MriStudio/MriCloud expand, the continued and most updated user supports is of the highest importance. Resource update: We continue to update them by incorporating state-of-the-art technologies and new atlas resources. The update of data I/O with ever-changing file formats (DICOM and proprietary) by the four major MRI manufactures remains essential. Aim 2: Extension of the functionality Addition of new services: We continue to work with our collaborators to incorporate further processing pipelines including structure-based lesion-load analysis using FLAIR images, MR spectroscopy, and quantitative susceptibility mapping data. QC report: We will develop and incorporate three levels of check points for quantitative data quality control. Aim 3: Integrative analysis platform: The co-existence of an array of MR image analysis pipelines within the same cloud platform provides a unique opportunity to perform multi-modal integrative analysis. We will develop tools to combine anatomical and physiological features from multiple MR contrasts and characterize unique features of a disease of interest. Aim 4: Bring to bedside: This is an exploratory aim to develop and test our cloud system as a platform for translational research.